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  1. The widely accepted definition of Internet fake news is: fictitious articles deliberately fabricated to deceive readers”.[1]
  2. Social media and news outlets publish fake news to increase readership or as part of psychological warfare.[1]
  3. This exposition analyzes the prevalence of fake news in light of the advances in communication made possible by the emergence of social networking sites.[1]
  4. You’re developing an algorithm to defend against fake news.[2]
  5. Shu: We proposed a model called “Defend,” which can predict fake news accurately and with explanation.[2]
  6. The idea of Defend is to create a transparent fake news detection algorithm for decision-makers, journalists and stakeholders to understand why a machine learning algorithm makes such a prediction.[2]
  7. In terms of the news content, fake news often includes some sentences that are fake, but others may not be fake.[2]
  8. The origin of the story might be dubious, but it doesn’t prevent the “fake news” story from accumulating 1.5 million likes across multiple platforms in just four days.[3]
  9. But while the task to detect fake news may sound daunting, there are several promising methods at researchers’ disposal.[3]
  10. “Visual presentation plays a huge role in people believing in fake news content.[3]
  11. Detection of fake news online is important in today's society as fresh news content is rapidly being produced as a result of the abundance of technology that is present.[4]
  12. In the world of false news, there are seven main categories and within each category, the piece of fake news content can be visual- and/or linguistic-based.[4]
  13. In order to detect fake news, both linguistic and non-linguistic cues can be analyzed using several methods.[4]
  14. Fake news can be found through popular platforms such as social media and the Internet.[4]
  15. Here we have tried to systematically discuss fake news, their definitions, causes, and reasons for propagation, and the available tools and detection techniques for fake news in a lucid manner.[5]
  16. All such queries have been explored along with the available datasets and algorithms for fake news detection.[5]
  17. The bigger problem here is what we call “Fake News”.[6]
  18. When someone (or something like a bot) impersonates someone or a reliable source to false spread information, that can also be considered as fake news.[6]
  19. In this short article, I’ll explain several ways to detect fake news using collected data from different articles.[6]
  20. Text analytics and NLP can be used to work with the very important problem of fake news.[6]
  21. More specifically, the approach analyzes how a fake news article propagates differently on a network relative to a true article.[7]
  22. A combination of both creates a more robust hybrid approach for fake news detection online.[7]
  23. In this paper, we propose a solution to the fake news detection problem using the machine learning ensemble approach.[7]
  24. The truthful news articles published contain true description of real world events, while the fake news websites contain claims that are not aligned with facts.[7]
  25. Fake News Detection (QcFND) in this paper, which exploits the technologies from Software-Defined Networking (SDN), edge computing, blockchain, and Bayesian networks.[8]
  26. Online fake news is a specific type of digital misinformation that poses serious threats to democratic institutions, misguides the public, and can lead to radicalization and violence.[9]
  27. While there have been multiple attempts to identify fake news, most of such efforts have focused on a single modality (e.g., only text‐based or only visual features).[9]
  28. We then perform a predictive analysis to detect features most strongly associated with fake news.[9]
  29. The experimental results indicate that a multimodal approach outperforms single‐modality approaches, allowing for better fake news detection.[9]
  30. In this paper, we propose some novel approaches, including the B-TransE model, to detecting fake news based on news content using knowledge graphs.[10]
  31. Firstly, computational-oriented fact checking is not comprehensive enough to cover all the relations needed for fake news detection.[10]
  32. Our approaches are evaluated with the Kaggle’s ‘Getting Real about Fake News’ dataset and some true articles from main stream media.[10]
  33. However, these advantages meanwhile enable “fake news,” i.e., news carrying intentionally and verifiably false information to spread widely and rapidly among social media users.[11]
  34. Two different studies conducted in 2016 found that 23% of Americans say they have shared fake news stories, either knowingly or unknowingly.[11]
  35. However, the fast and massive spreading of fake news can rapidly cause inestimable social harm.[11]
  36. The prevalence of fake news on social media and its serious negative impacts have become a primary concern of the general public.[11]
  37. However, social media also enables the wide propagation of "fake news," i.e., news with intentionally false information.[12]
  38. Fake news on social media can have significant negative societal effects.[12]
  39. Therefore, fake news detection on social media has recently become an emerging research area that is attracting tremendous attention.[12]
  40. The concepts, algorithms, and methods described in this lecture can help harness the power of social media to build effective and intelligent fake news detection systems.[12]
  41. The goal is to give you a gentle introduction to automated fake news detection.[13]
  42. Fake news refers to information content that is false, misleading or whose source cannot be verified.[13]
  43. But first, we need to understand the types of fake news detection being used.[13]
  44. There are various techniques and approaches implemented in fake news detection research.[13]
  45. In particular, beguiling content, such as fake news made by social media users, is becoming increasingly dangerous.[14]
  46. The fake news problem, despite being introduced for the first time very recently, has become an important research topic due to the high content of social media.[14]
  47. The main challenge is to determine the difference between real and fake news.[14]
  48. In this paper, a two-step method for identifying fake news on social media has been proposed, focusing on fake news.[14]
  49. With the wide spread of Social Network Services (SNS), fake news—which is a way of disguising false information as legitimate media—has become a big social issue.[15]
  50. This paper proposes a deep learning architecture for detecting fake news that is written in Korean.[15]
  51. 1 Introduction Automatic fake news detection has become increasingly important with the rapid development of social media.[16]
  52. Adopting different strategies for fake news detection is one of the most fundamental research directions.[16]
  53. To date, most studies focus on detecting fake news in a specific language, where English is the most commonly studied language (Pérez-Rosas & Mihalcea, 2014).[16]
  54. It raises an important question about the applicability of existing methods for detecting fake news.[16]
  55. Fake news has become an important topic of research in a variety of disciplines including linguistics and computer science.[17]
  56. And this need for data leads to our call to arms to the research community, to news media and social media companies: We want your fake news data.[17]
  57. Then in the section on Approaches to the fake news problem, we discuss general approaches, from multiple points of view (educating the public, stopping the spread, human and automatic identification).[17]
  58. The approach we take concentrates on automatic identification by using the text of the fake news article (rather than metadata of information about spread).[17]
  59. But on the other hand, disinformation and hoaxes that are popularly referred to as “fake news” are accelerating and affecting the way individuals interpret daily developments.[18]
  60. The news industry must provide high-quality journalism in order to build public trust and correct fake news and disinformation without legitimizing them.[18]
  61. Technology companies should invest in tools that identify fake news, reduce financial incentives for those who profit from disinformation, and improve online accountability.[18]
  62. Fake news is generated by outlets that masquerade as actual media sites but promulgate false or misleading accounts designed to deceive the public.[18]

소스

  1. ↑ 1.0 1.1 1.2 Detecting Fake News in Social Media Networks
  2. ↑ 2.0 2.1 2.2 2.3 An algorithm to detect fake news: A Q&A with Huan Liu and Kai Shu
  3. ↑ 3.0 3.1 3.2 The Fake News Detector
  4. ↑ 4.0 4.1 4.2 4.3 Detecting fake news online
  5. ↑ 5.0 5.1 Fake News Detection: Tools, Techniques, and Methodologies
  6. ↑ 6.0 6.1 6.2 6.3 Detecting Fake News With and Without Code
  7. ↑ 7.0 7.1 7.2 7.3 Fake News Detection Using Machine Learning Ensemble Methods
  8. ↑ Edge Computing and Blockchain for Quick Fake News Detection in IoV
  9. ↑ 9.0 9.1 9.2 9.3 Detecting fake news stories via multimodal analysis
  10. ↑ 10.0 10.1 10.2 Content Based Fake News Detection Using Knowledge Graphs
  11. ↑ 11.0 11.1 11.2 11.3 FNED: A Deep Network for Fake News Early Detection on Social Media
  12. ↑ 12.0 12.1 12.2 12.3 Detecting Fake News on Social Media
  13. ↑ 13.0 13.1 13.2 13.3 Introduction to Automated Fake News Detection
  14. ↑ 14.0 14.1 14.2 14.3 Fake news detection within online social media using supervised artificial intelligence algorithms
  15. ↑ 15.0 15.1 Fake News Detection Using Deep Learning
  16. ↑ 16.0 16.1 16.2 16.3 Cross-Language Fake News Detection
  17. ↑ 17.0 17.1 17.2 17.3 Big Data and quality data for fake news and misinformation detection
  18. ↑ 18.0 18.1 18.2 18.3 How to combat fake news and disinformation

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  • [{'LOWER': 'post'}, {'LOWER': '-'}, {'LEMMA': 'truth'}]
  • [{'LOWER': 'alternative'}, {'LEMMA': 'fact'}]
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